Update 31. 07. 2026 – Appendix B This Appendix B extends the experimental validation reported in Appendix A from architectural and end-to-end testing to the first controlled adversarial evaluation of the operational ZEUS X-Trust prototype. The new appendix documents a complete campaign of 500 real password-based attack attempts across 14 attack profiles, executed over approximately 5. 4 hours against one continuously enrolled live neural instance. The campaign included both reference-aware attacks derived from partial or complete knowledge of the enrollment password and reference-blind attacks such as brute-force samples, dictionary patterns, leetspeak mutations, semantic guesses, rule-based combinations, typographical attacks, and hybrid profiles. The final result was: 500 DENY 0 unauthorized ACCEPT 0 processing errors 0 breach events The appendix explains how the attacks were processed through the full ZEUS X-Trust protection path. Of the 500 adversarial inputs, 482 reached neural execution. Among these, 377 produced addressable positional checks and supported full position-bound amplitude-drift analysis. A further 105 reached the neural process but addressed no enrolled positions and were rejected as unknownchars. The remaining 18 empty or non-printable inputs were rejected by the character-set guard before neural execution. The new results show that rejection was not based on a single binary rule. Unauthorized inputs failed through different mechanisms, including invalid input structure, absence of addressable positions, positional mismatch, amplitude mismatch, or the combined failure of the enrolled map-profile-instance relation. Appendix B also documents: the operational fail-closed prototype logic; the definition of unauthorized ACCEPT and actual breach; the use of one unchanged live neural instance throughout the full campaign; approximately 145, 000 cumulative neural iterations without progressive convergence toward an unauthorized state; the measured security distance between the valid reference and rejected attack states; the distribution of amplitude drift across multiple mapped neural layers; and the preservation of numerical stability throughout the campaign. The intentionally simple six-character enrollment password demonstrates that ZEUS X-Trust security is not determined by password length alone. The password selects the authentication geometry, while the complete protection relation is formed by the password-controlled positional map, the live amplitude profile, and the specific enrolled neural instance. This appendix was necessary to move ZEUS X-Trust beyond experimental feasibility and functional prototype testing into documented adversarial validation under real operating conditions. The results provide the technical basis for the next development phases: system-level attacks against the surrounding implementation; independent watchdog integration; amplitude-proximity-based attack escalation; continuous live-state integrity monitoring; and controlled external black-box validation without disclosure of the proprietary source code, positional maps, reference amplitudes, or internal architecture. _______________________ Update 25. 07. 2026 - Appendix A This Appendix A documents the experimental validation of ZEUS X-Trust from stable amplitude-state formation to an integrated live-state authentication prototype. It validates Stages 3–5 across four original ZEUS X-Trust architectures, including stability, separability, deterministic password-controlled mapping, instance binding, and end-to-end authentication. It demonstrates that incorrect passwords are rejected through positional mismatch, while newly initialized instances are rejected through amplitude mismatch. It documents the complete enrollment, login, deny, no-overwrite, restart, and re-enrollment logic required for an operational prototype. It was necessary to move ZEUS X-Trust from a theoretical architecture to an experimentally defined and reproducible authentication workflow. The results provide the technical basis for operational deployment, adversarial testing, and the planned watchdog-based continuous integrity monitoring _______________________ Preprint This record presents a substantive new research preprint in the ZEUS X-Trust / IGAN research line. It continues and substantially extends the earlier work “AI-Powered Quantum-Resistant Authentication and Key-Management System” by reframing the architecture as a ZEUS-derived Information-Geometric Amplitude Network approach for live-state authentication, amplitude-bound key states, watchdog-recorded IGAN reference values, and runtime integrity validation. ZEUS X-Trust / IGAN proposes an authentication and key-management architecture that shifts the primary security object away from conventional stored hashes, static keys, token objects, or recoverable credential material. During enrollment, the password acts both as a map into the IGAN amplitude space and as a state generator or selector for the initial valid IGAN amplitude-state configuration. During runtime authentication, the password no longer continuously generates the neural state. Instead, it acts only as an addressing map that selects the relevant IGAN amplitude positions. The running IGAN provides the currently observed values at those same positions, while the watchdog compares them against the corresponding IGAN reference values recorded during enrollment. The paper introduces the following core concepts: - ZEUS X-Trust as a ZEUS-derived authentication and key-management architecture; - Information-Geometric Amplitude Networks as the measurable amplitude-state layer; - password-controlled mapping into selected layer, neuron, and amplitude positions; - amplitude-bound key states as live validation relations rather than static bitstrings; - watchdog-based comparison of current IGAN values against enrolled IGAN reference values; - prototype measurements on fixed-point formation, amplitude separability, final-amplitude distributions, and post-fixed-point drift; - the security implications of no useful verification oracle, coupled map-and-value unknowns, instance-specific amplitude-state behavior, and watchdog-enforced rejection outside configured tolerance windows; - open engineering constraints including tolerance calibration, profile-vector validation, watchdog hardening, reference protection, replay/substitution testing, redundancy, recovery, and attacker-model-specific entropy analysis. The broader theoretical context is situated in the author’s ZEUS framework and the information-first model developed in “The Structure of Reality, ” where information is treated as ontologically primary and physical, geometric, and computational structures are interpreted as derived information-state relations. In the present work, this framework is narrowed to the operational question of whether information-geometric amplitude states can be used for authentication, key management, and live-state integrity validation. The external references in the paper are not presented as foundations from which ZEUS X-Trust / IGAN was derived. They are used to delimit the surrounding research landscape and to distinguish the proposed mechanism from adjacent work in neural password authentication, neural key binding, PUF-style reference comparison, and Zero Trust security. To the author’s knowledge, no identified prior work combines password-controlled mapping into a live neural or information-geometric amplitude/state space, enrollment of reference values at password-mapped state positions, and runtime validation of the currently observed values at those same positions through watchdog logic within a tolerance window. This record is a research preprint and prototype-positioning document. It is not presented as a finalized production cryptosystem. The current results support further validation of password-mapped amplitude profiles, full layer-/neuron-/amplitude profile vectors, collision thresholds, tolerance windows, and watchdog-enforced runtime integrity. Architectural details that would enable unsafe replication, misuse, or premature operational claims are intentionally limited. This work continues the earlier preprint “AI-Powered Quantum-Resistant Authentication and Key-Management System” and should be read as a substantive continuation of that research line, not as an unrelated standalone publication.
Stefan Trauth (2026) studied this question.